AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation
This addresses the problem of automating financial analysis for investors or analysts, but it is incremental as it builds on existing LLM capabilities with a new evaluation method.
The paper tackled generating financial reports from time series data using large language models, proposing a framework with prompt engineering and an automated highlighting system for evaluation, and demonstrated that LLMs can produce coherent reports on real and synthetic data.
This paper explores the potential of large language models (LLMs) to generate financial reports from time series data. We propose a framework encompassing prompt engineering, model selection, and evaluation. We introduce an automated highlighting system to categorize information within the generated reports, differentiating between insights derived directly from time series data, stemming from financial reasoning, and those reliant on external knowledge. This approach aids in evaluating the factual grounding and reasoning capabilities of the models. Our experiments, utilizing both data from the real stock market indices and synthetic time series, demonstrate the capability of LLMs to produce coherent and informative financial reports.